From big data, in form of huge text corpora, we automatically extract sentences that may contain definitions and present them to a user (lexicographer/specialist) working on a particular concept; then the user has a possibility to simply re-use a particular definition, or adjust one of them according to others.

the Czech web corpus results are much worse than in English -we do not have a good explanation of this interesting observation, perhaps the Czech internet contains less educative texts.


Wikipedia is considered an excellent source of texts for IE systems due to its broad variety of topics and advantageous characteristics such as the quality of the texts and their internal structure.

• How does information obtained from dictionaries compare to information obtained from encyclopedias?


We did not, however, evaluate results obtained from the English Wiktionary.

these resources could also be combined since the information contained in each one complements the others.


Both kind of approaches were studied for the case of English-Basque pair on (Saralegi and Lopez de Lacalle, 2010).

the results correspond very well to the improvement in translation quality reported in the previous section.


The third strategy, which uses the translation of the initial query as context tr("PS 2 joku")="PS 2 game", provides a correct translation tr(qr)="PS 2 game new".

only one team achieved a significant statistical improvement.


Since most Vietnamese words are composed of more than one syllable where each syllable is separated by blanks (Dinh et al., 2008), using common tokenizers such as replacing blanks with word boundaries does not work for Vietnamese.

the CRFbased tool of the group (Tu et al., 2006), the PVnSeg tool (unknown source) and the hybrid method of (Hong Phuong et al., 2008) are compared using a test corpus containing 1,264 articles from Politics-Society section of a Vietnamese online newspaper "Tuoi Tre", where words have been manually segmented by linguists.


Then, in Section 3, we provide an overview of the Saffron system which we use for generating the dataset.

the related work in this area can be roughly categorized in two main directions.


Figure 1 shows a future forecast for some example keywords based on individual polynomial models.

solutions based on citation analysis are less appropriate for contemporary analysis of emergent trends, as this type of data is less robust for recent documents and can not be applied as soon as documents become available.


There have been efforts to create extractive summaries (Abu-Jbara and Radev, 2011;Qazvinian et al., 2013) and flows of scientific ideas (Shahaf et al., 2012).

from survey responses, we create the structured representation deterministically.


Ideally, annotators would highlight relevant information in the text and link it to the structured representation.

ideally, annotators would highlight relevant information in the text and link it to the structured representation.


We have described the creation of a corpus of clinical text, Asthma Timelines, annotated for asthma status and other supporting information.

the final S 3 -step is shown to not improve results on any metric, suggesting that the finite number of errors in human annotation may have been largely corrected.


This is due to the fact that the S-step is where new rules in the system will posit more (and perhaps inaccurate) matches.

this context includes the characteristics of the underlying patient population that was sampled from the EMR, as well as what data was available from the implementation of the EMR.


As English is OUP's largest dataset and it is used as a pivot language for translations, querying response times tend to be greater, so the source filter was particularly useful for performance optimisation and content separation.

due to the lack of maturity of the triplestore technology, scaling up has proved to be a challenging experience; this has occasionally moved us towards rethinking some of the modelling decisions.


For instance, let's consider that the API endpoint that provides the data for a dictionary headword is invoked to retrieve the headword Book in English.


For example, assuming English to isiZulu and an English to Northern Sotho datasets exist, we are able to extract isiZulu to Northern Sotho translations.

for example, as the number of ingested datasets was growing, a need was discovered for a filtering mechanism both for speeding up search, and for distinguishing between these datasets.


The above principles should be established once and for all, particularly for LRs created on the basis of the reuse of public data and/or funded by public money.

language corpora, both parallel and monolingual are considered most important for instance for MT, not only SMT but also hybrid MT.


In addition to access to LAPPS Grid tools and data, we have developed and contributed the following capabilities of the LAPPS Grid for use in Galaxy in order to support NLP research and development within that platform, including: 1. exploitation of our web service metadata to allow for automatic detection of input/output formats and requirements for modules in a workflow and subsequent automatic invocation of converters to make interoperability seamless and invisible to the user; 2. incorporation of authentication procedures for protected data using the open standard OAuth 9 , which specifies a process for resource owners to authorize third-party access to their server resources without sharing their credentials; and 3. addition of a visualization plugin that recognizes the kind of input (coreference, phrase structure) and then uses appropriate off-the-shelf components like BRAT and Graphviz to generate a visualization.

an additional, and potentially hugely significant, outcome of the LaPPS/Galaxy collaboration is that it enables the use of LaPPS Grid NLP services to extract information from repositories of biomedical publications such as PubMed 14 and pass it on to biomedical analysis and visualization tools available in Galaxy.


However, the field of NLP research and development has been plagued by a chronic lack of potential for replicability of results, as discussed in several recent publications (Pedersen, 2008;Fokkens et al., 2013), blogs 10 , and workshops 11 .

docker 13 allows users to package an application with all of its dependencies into a standardized unit into a docker image, which is an easily distributable full-fledged installation that can be used for testing, teaching, and presenting new tools and features.


In order to facilitate the discovery of the data needed for the project, the criteria should be as specific as possible and should also cover all aspects of the data use.

each corpus comprises about 15,000 tweets and 10% of these data are also considered for double annotation.


Mirroring body behaviours, and especially facial expressions, are common in social interactions and are important social cognitive mechanisms since they enable the observer to understand not only the goal of an observed motor act, but also the intention behind it (Rizzolatti, 2005;Rizzolatti and Fabbri-Destro, 2008).

there seems to be a correlation between co-occurring Dominance values (p = 0.0059).


We will next explore supervised and ensemble methods.

solid lines represent high cosine similarity between a pair of captions.


The next section presents related work in the field of eye-tracking during reading tasks.

and summarised in (Rayner, 1975;Rayner, 1998;Rayner et al., 2012).


Data inaccuracies usually result from poor calibration or system imprecisions typical of all eye trackers (Duchowski, 2009).

the corpus presented in this paper suffers from several limitations, which need to be taken into account when designing experiments involving this data.


The selected 9 control participants were also divided into two significantly different groups of "skillful" and "less skillful" readers (U = 5724, N1 = 5, N2 = 4, p < 0.001, two-tailed) ( Table 2).

the main issue with the language assistance tools mentioned above is that they need to rely on robust research into the reading difficulties people with autism face and the specific linguistic components which need to be simplified.


Mean age (m) for the ASD group was m = 30.75, with standard deviation SD = 8.23, while for the control group it was m = 30.81, SD = 4.8.

we had Group A readers including 4 autistic and 4 non-autistic "skillful" readers, which did not differ significantly in terms of answering scores (U = 5512, N1 = N2 = 4, p = 0.129, two-tailed) and a Group B readers consisting of 5 autistic and 5 non-autistic "less skilful" readers with no statistically significant difference in their answers (U = 8244, N1 = N2 = 5, p = 0.193, two-tailed), as shown in Table 2.


One of the biggest challenges in the design of this study was to decide on such a number of texts to be assessed by each participant that would not cause fatigue in them and thus be in bridge with ethical requirements.

until now, eye tracking has not been used to investigate reading in autism, possibly due to the number of procedural difficulties related to this kind of research with autistic participants (Section 3), and thus there is no reliable information about the particular types of phrases which need simplification for readers with autism.


It can be seen that the MI.log_f and MI scores ranked as the best AMs for predicting the right collocates of the Arabic keyword list.

it can be seen that the Mi.log_f and Mi measures achieved the highest MAP scores with a MAP score of over 0.85, while the t-score and Mi3 were the least useful scores in terms of identifying FSs among the high frequency lexical items, with MAP scores below 0.50.


The MWT obloga trake 'belt coating' has the structure N2X (a noun followed by a noun in the genitive), and its components are nouns obloga 'coating' and traka 'belt'.

this research was supported by the Serbian Ministry of Education and Science under the grant #47003 and #178003.


(2) Vojáci dostali od velitele rozkaz střílet.

the number of CPs in the PDT is limited to 2,778 instances of CPs in 2,558 sentences.


Combining the output from annotators is required since some NEs have been discovered by both annotation engines, while others come from only one annotator.

one can also observe that only a little more than 50% of all NEs are associated with a KB node.


The semantic types of db:General_Motors and db:United_States_dollar correspond to the semantic types suggested by the hybrid module based on the textual context of these mentions, which are Organization and Miscellaneous, respectively.

this knowledge enables an entity linking system to model and perform reasoning over the semantic context of the real-world entities it links to.


We propose an adaptive entity linking approach consisting of two steps ( Figure 1): a general-purpose hybrid module and a domain adaptation module.

an intelligent system that anchors entities from text to existing entities in a knowledge base, namely an entity linking system, could benefit from the structured semantic knowledge describing those entities in the knowledge base.


At the first stage, we attained distributed representations of words by employing a fast unsupervised learning method on a large unlabeled corpus.

most of our NER models are trained on annotated Turkish news data by Tür et al.


With this motivation, we explored both local and non-local features but observed that we achieve better results without non-local features.

all-capitalized, is-capitalized, all-digits, contains-apostrophe, and is-alphanumeric.


Entity Linking (EL), such as the EL track at NIST Text Analysis Conference Knowledge Base Population (TAC-KBP), aims to link a given named entity mention from a source document to an existing Knowledge Base (KB) (Ji et al., 2014).

every vertex (m, c) has an initial similarity score iSim(m, c) between m and c. We split m and c into sets of tokens T m and T c and recognize two cases: 1) if T m and T c have any tokens in common then their similarity is 1.0; 2) otherwise it is a reciprocal of the edit distance between m and c: the pairwise initial similarity for "Buenos Aires" vs "Buenos Aires Wildlife Refugee" and for "Buenos Aires" vs "University of Buenos Aires" equals to 1.0.


As we can see the number of Company and Product entities in the Synthesio corpus is almost four times more than in the Ritter Corpus.

we present some results of domain adaptation on this corpus using a labelled Twitter corpus (Ritter et al., 2011).


Irish is a VSO language on the Celtic branch of the Indo-European language family tree.

mETEOR is based on the harmonic mean of precision and recall, whereby recall is weighted higher than precision.


Although the evaluation showed improvement as more data is added to IRIS, the manual evaluators annotated the translation quality rather low.

within the main dialects however, the language is even more diverse, with sub-dialects spoken by individual language communities.


To support this, a language model is created where the phrasal patterns (sequences of tokens) are recorded, together with the frequency-of-occurrence of each pattern.

the algorithmic steps are then described that form the process for augmenting the language model.


Improvement has been reported when translated from French (+1.6 BLEU), German (+1.95 BLEU) or Hungarian (+1 BLEU) into English.

obtained three translations for each chunk are then evaluated and the best translation for the chunk is selected.


The corpus contains 1.4 million unique legal domain sentences.

seven different translations for each source sentence were obtained.


Nutch follows the standard IR model of Lucene 6 with document parsing, document Indexing, TF-IDF calculation, query parsing and finally searching/document retrieval and document ranking.

the collected corpus is noisy and contains some non-German as well as non-English words 7 http://www.statmt.org/wmt15/translation-task.html and sentences.


When the translator selects the colour-coded TM alternative (c.f., Figure 3), the given input sentence is also colour-coded to reflect the matching and unmatching parts in the input sentence.

the substitution log can serve as a valuable resource for training a statistical automatic post-editing system.


Since the data collection is task based, we thought the subjects would be focused on providing and receiving clear instructions.

3 The ILMT-s2s System is activated by a "Push to talk" button that the subject will click-and-hold for the duration of the utterance, and release once the subject has finished the utterance.


This test set contains English transcriptions of 12 TED conference talks (and their French translations), selected in such a way that the texts include a reasonable number of instances of some less frequent pronoun types.

previous approaches to pronoun translation evaluation include the automatic precision/recall-based measure of Hardmeier and Federico (2010), the (manual) pronoun selection task used in the DiscoMT 2015 shared task evaluation (Hardmeier et al., 2015) and methods based on manual counting (Le Nagard and Koehn, 2010;Guillou, 2012;Novák et al., 2013).


The systems generally performed well on the translation of addressee reference "you", as compared with the baseline.

the manual annotation of the DiscoMt2015.test dataset was funded by the European Association for Machine translation (EAMt).


Out-of-vocabulary (OOV) word is a crucial problem in statistical machine translation (SMT) with low resources.

the BtEC corpus is a multilingual speech corpus containing tourism-related sentences.


@5) """) """)  There are many publicly available word embedding toolkits.

we used the skipgram, which inputs each current word to a log-linear classifier with a continuous projection layer, and predicts its context words within a certain window.


This deterministic handling of Twitter-specific syntax is applied to all further experiments in Table 3.

our experiments use a standard trigram HMM tagger 3 (Brants, 2000) and the openNLP maximum entropy tagger.


All experiments are performed on the development part of our dataset.

we are interested in how much parse quality can be gained by text normalization.


We believe there are two reason for this phenomenon: the first is that the QUESTION samples in the training set are not sufficient; the second, which we consider as the more crucial one, is that the sample we apply, a 5-word sequence, does not cover the sentence beginning, where the typical question pattern locates, such as "what do you ..." or "how can I ...".

obviously, our proposed models failed to predict even one question mark in the test dataset.


Mini-bached AdaGrad (Duchi et al., 2011) and dropout (Srivastava et al., 2014) are used for optimization.

to our knowledge, this is the best speed result in the literature for constituent parsing.


D1: The distance between a dependent word w i and its candidate head w j .

with these models, we can figure out which annotation is effective for raw text parsing.


In the latter, mere numbers are used as a notation for the position of the token with respect to the next one in the input, numbered 0.

the annotation style of the It-tB resembles that used for the so-called analytical layer of annotation of the Prague Dependency treebank for Czech.


The proposed approach applies the Lexicon-Grammar (LG) framework and its language formalization methodologies, developed by Maurice Gross during the '60s.

the previous automaton may process a query as the following one: (1) Tutti gli archeologi che sono stati anche scrittori nati nel '900 (All the archaeologists who have been also writers and were born in 19th century) 3 .


The AMs in this procedure use (stacked) MFCC features and were trained using the Kaldi toolkit (Povey et al., 2011).

hence, more anchor segment data can be extracted as training material for the next AM which, in turn, will hopefully lead to an AM recognizing a larger amount of speech correctly.


The resulting diagnoses of the ILSE participants are shown in Table 3.

speaking style and Crosstalk: The speaking styles of interviewers and participants differ substantially: While the semi-standardized questions of the interviewers are usually short and well planned, participants answer in detail, great length, and in a very spontaneous fashion.


the position and orientation of the microphone placed on the table.

as expected, the outof-vocabulary (OOV) rate of the training word types for the interviewers' speech is considerably lower than for the participants' speech.


These sequences are then split at speaker turns and used to train a new AM for the next iteration.

we can make use of the transcriptions which originally could not successfully be aligned with the audio.


if inter-speaker variability is an important factor, intra-speaker variability is not less important (Kahn et al., 2010).


The talent was also instructed to be consistent in pausing in case the corpus is to be used for prosody modelling.

to choose criteria for iteratively choosing sentences, a simple count was adopted where each sentence was scored by the following formula: Where ( , ) is the "Sentence Score" of the sentence relative to corpus , ( ) is the "Sentence Unit Frequency" which is the number of times a specific unit indexed by appears in the sentence and ( ) is the "Corpus Unit Frequency" which is the number of times a specific unit indexed by appears in the corpus .


Knowing that the speakers had a constant gait velocity, we resampled the resulting skeleton tracks by considering linear interpolation, which yielded data points with equally spaced time-intervals.

then we determined the point on the trajectories where the squared error of a detection exhibited the global minimum, and mapped the detection to this point of the trajectory (see Fig.


The XML document contains the same speech fragment that is depicted in Figure 1.   xml version="1.0" encoding="UTF-8"?> <files> <file name="[Recording Name]"> <fragment place="2" recorder="2" speaker="8" type="2"> <part length="0.52" audio_file="[File Path]" >(0.52)</part> <part length="1.611" audio_file="[File Path]" >čau <c type="exclamation_mark"> izsaukuma zīme </c></part> <part length="0.528" audio_file="[File Path]" >(.h)</part> <part length="1.482" audio_file="[File Path]" >nopērc lūdzu pienu</part> <part length="1.119" audio_file="[File Path]" >(.

this allowed us to identify issues in the annotations that would possibly corrupt the data in our speech recognition system training workflows (e.g., incorrect number of words in the orthographic annotation that a spelling correction is linked to, unclosed tags, overlapping tags, etc.).


This means that only the SAT was repeated.

the acoustic models get more adapted to the 100 hours of non-dictation speech data.


There are some children's speech databases for EP, such as Speecon with rich sentences (Speecon Consortium, 2005); ChildCAST (Lopes, 2012;Lopes et al., 2012) with picture naming; the Contents for Next Generation (CNG) Corpus targeting interactive games (Hämäläinen et al., 2013) and (Santos, 2014;Santos et al., 2014) with childadult interaction.

<mãe> [mˈɐj] 2 and <bem> [bˈɐj]); the presence of consonant clusters (e.g.


According to the outcomes of the efficacy tests presented in (Beijer et al., 2014), the user satisfaction appears to be quite high.

the first dysarthric speech data collection of Dutchspeaking patients aimed to use the data for a pilot study to investigate the performance of speech-to-text systems on deviant speech (Sanders et al., 2002).


These corpora consist of single and 1 According to Ethnologue: www.ethnologue.com/ language/urdLast visited: 04-03-2016 2 DUC: www-nlpir.nist.gov/projects/duc/ Last visited: 04-03-2016 3 TAC: http://www.nist.gov/tac/ Last visited: 04-03-2016 multi-document summaries written by humans.

they contain real text as written by the native speakers.


We further apply normalization, part-of-speech tagging, morphological analysis, lemmatization, and stemming for the articles and their summaries in both versions.

urdu Morphological Analyzer is built in Haskell (Marlow, 2010) (using Functional Morphology Toolkit (Forsberg and Ranta, 2004)), but it is not updated from a long time.


Our three freelancers (2 men and 1 woman) are university student and native speaker of Bahasa Indonesia.

as discussed in previous section, we utilize Whatsapp, one of the most well-known online instant messaging application, to construct the summarization corpora.


Most of these logs contain conversations discussing daily activity such as: soccer group, running hobby, faculty organization, family group, and software team development as shown in Table 1.

we construct the first ever Indonesian corpora for chat summarization by employing three native speakers to manually build the summary.


Our method is based on the well established linguistic premise that semantically related words occur in similar contexts (Turney et al., 2010).

table 2 also shows the results of other SERA variants including discounting and query reformulation methods.


It then uses the KL divergence between the document and the summary content models for selecting sentences for the summary.

the content quality of a given candidate summary is evaluated with respect to this pyramid.


Since its introduction, ROUGE has been one of the most widely reported metrics in the summarization literature, and its high adoption has been due to its high correlation with human assessment scores in DUC datasets (Lin, 2004).

these lists of results are based on a rank cut-off point n that is a parameter of the system.


Our aim is to analyze the effectiveness of the evaluation metrics, not the summarization approaches.

we asked two human annotators to review the gold summaries and extract content units in these summaries.


For instance, comment sentences linked to the same article sentence can be seen as forming a "cluster" of sentences on a specific point or topic.

i daresay we can 'adapt' to a certain extent but there are limits.


discusses online forums and the OnForumS corpus creation, Section §3.

the crowdsourcing Human Intelligence Task (HIT) was designed as a validation task (as opposed to annotation), where each system-proposed link and labels are presented to a human contributor for their validation with both article sentence and comment sentence placed within context (see Fig.


WordNet represents a cornerstone in the Computational Linguistics field, linking words to meanings (or senses) through a taxonomical representation of synsets, i.e., clusters of words with an equivalent meaning in a specific context often described by few definitions (or glosses) and examples.

the former is a top-down and often human-generated representation of a domain whereas the latter comes from free tags associated to objects in different contexts.


For this reason, the algorithm Then, for each synset S i we compute the set of all candidate semantic ConceptNet triples P conceptnet (S i ) as the union of the triples that contain at least one of the terms in T i .

it aimed at enriching WordNet with semantics containing direct relations-and words overlapping, preventing associations of semantic knowledge on the unique basis of similarity scores (which may be also dependent on algorithms, similarity measures, and training corpora).


We have ensured a fully random selection of verbs to annotate.

on the whole, there are so few alternative readings in the data set that they could hardly harm the interannotator agreement, so we have not processed them in any sophisticated way we had been considering before obtaining the results.


Before drawing any conclusions from the data, we measured the interannotator agreement.

we had speculated that WSsim/USim data might have been slightly easier to annotate, given the lemmas processed: while VPS-GradeUp contains only Figure 2: The annotation form using Google Forms verb lemmas (29), WSsim contains 11 lemmas of various parts of speech (4 verbs: add, ask, order, and win, 5 nouns: argument, function, interest, investigator, and paper, and 2 adjectives: important and different).


(GermaNet provides twelve such relation types, which are all (sub)classes of meronymy/holonymy, entailment, causation, and association.)

we next consider the semantic relations that link the successfully annotated target senses to their lexical substitutes.


Finally, we note that the majority of substitutes cannot be reached by following semantic relations of a single type.

the latter basically instructed the adjudicator, for each instance on which the annotators disagreed, to accept one or the other set of annotations, or the union of the two.


This metric, borrowed from information retrieval, measures the accuracy of each cluster with respect to its best matching gold class: where Ω = {ω 1 , ω 2 , .

olympionike ("olympian") suggests that one of the annotators has exploited his or her real-world knowledge of the context's subject (in this case, Hollywood actor and competitive swimmer Johnny Weissmuller).


our classifiers are able to distinguish between literal and idiomatic uses of infinitive-verb compounds with quite a high degree of precision.


Spelling became a political issue in Germany through the German orthography reform, which was decreed in 1996 and caused a fierce controversy carried out in public as well as uncertainty even among professional writers about the correct spelling.

to see the influence of individual groups of features, we performed an ablation test (see Figure 1): Disappointingly, selectional preference information has a slightly negative effect.


The better the annotators agree on a particular pattern -concordance match, the smaller the range of their judgments is.

the WSD decisions associated with a given sentence ID are copied to all relevant rows.


We hope that this research will help with a more nuanced evaluation of the classification and entry-building tasks.

2 Median and Range translate as "Goodness of match between a pattern and a concordance" and "Agreement on this goodness of match", respectively.


We also define class pmonb:Tag to capture (via property pmonb:tag) some specific annotations of markables (e.g., PRD, REF, SUPPORT) in the examples.

preMOn is freely available and accessible online in different ways, including through a dedicated SpARQL endpoint.


NomLex-PT offers a list with some 4,240 pairs of related verb/noun forms in Portuguese.

since we have no Portuguese pair that directly corresponds to "apportion" and "apportionment", morphosemantic links generated from this pair, if any, are considered as connecting alocar-alocação in Portuguese.


To construct NomLex-PT, we semi-automatically translated the original English NomLex (Macleod et al., 1998), the French Nomage (Balvet et al., 2011), the Spanish AnCora-Nom (Peris and Taulé, 2011) and manually verified the pairs acquired.

the work we describe here consists in adding to the pairs of translated morphosemantic links, that is to pairs of senses of verbs/nouns in Portuguese, a label from Princeton's table and making such a triple, a link of the OpenWordnet-PT.


In order to bridge the gaps that currently separate the various existing morphological data resources and models described above, we developed the MMoOn Core model 23 .

the examination of the related works in the domain of morphological data revealed five types of language resources.


We plan to deliver the chapter of the letter bā' by the time of LREC 2016.

a similar treatment might be envisaged for schemes.


golpe de * que le, 'stroke of that his', third element), where * indicates the location of the target word in the context.

48), and it corresponds with the frequency of the complex inflected word (fortuna, 'fortune', second element) in its context (e.g.


Algorithm First, we modified the Spanish OpenThesaurus and created our List of Senses.

openThesaurus does not have information of what synonyms are simpler or more complex, something crucial to perform lexical simplification.


In this method, a lexicon is used to spell-check OCR-recognized words and correct them if they are not present in the dictionary.

furthermore, when many consecutive corrupted words are encountered in a sentence, it is difficult to choose good candidate words.


For this purpose, Optical Character Recognition (OCR) systems have been developed to transform scanned digital text into editable computer text.


We calculated the LDs between the focus variants and respectively their historical correction and their contemporary correct counterparts.

the essence is that all the characters in the alphabet are assigned a large numerical value which puts them apart in Euclidean space at fixed and known distances.


In fact, since we pursue unsupervised fully-automatic OCR post-correction, we should only be interested in the bestfirst ranking scores.

it is pitfalls like this that the Nederlab project (Brugman et al., 2016) is hoped to help prevent in the future, by allowing scholars to register exactly the collection of texts their researches were based on.


In this we are waylaid, faced with the more disappointing recall in our current evaluation.

it is the feature of digitized diachronic texts most often commented on by researchers working on digitized text (cf.


predicate argument structures) and various transformations such as alternations.

in argument expression, the patterns presented in the previous section are realized as language utterances with some specific syntactic constructions proper to argument expression (section 4.2).


Witnesses in their accounts of one and the same event will differ in a variety ways, for example in their choice of words or the attention they give to certain details by elaborating upon them.

we limited our material to the 9 interviews labeled in the database with tape numbers 208, 230, 348, 374, 488, 490, 508, 519, 549.


Note that '|' implies the potential segment position.

we annotated a bilingual discourse corpus in Chinese-English language pair, which contains nearly 20K sentences.


 Structure: discourse structure links EDUs together, mainly based on the structure of a sentence.

we resort to the monolingual EDU relation to determine the BEDU relation.


The two annotators classified 50 instances for each connective according to the PDTB 3.0 schema (27 senses) with the additional option of marking an instance as not being a connective.

we discussed different means of obtaining such semantic information, as well as our way of representing various kinds of ambiguities in the lexicon.


In as far as the lexical semantic content of discourse connectives is taken into account in the semantic computation, SDRT relations therefore can be mapped well onto connectives.

3 This way, we extracted between one and 19 attested possible relations for 126 connective types that occur in the PCC.


References to disabilities supports all kinds of syntactic, morphological, and semantic variations.

it focuses on the disabilities associated with a specific RD.


• Pendred syndrome (PDS) is a clinically variable genetic disorder characterized by bilateral sensorineural hearing loss and euthyroid goiter.

in this paper myopia, hyperopia, astigmatism, cataracts or glaucoma are not considered a disability since they are not permanent.


In training, we compute the PMI of a word pair wi and wj, where wi and wj are in ds1 and ds2, respectively, and select the top 30,000 word pairs of higher PMI.

the HIt Chinese discourse relation treebank (HIt-CDtB) is used to evaluate the proposed models.


The most important change is the addition of speechact relations in the sense of Sweetser (1990).

other studies that have used the PDTB framework to annotate spoken data are Demirşahin and Zeyrek (2014) and Stoyanchev and Bangalore (2015).


(5) but times have changed, even in Utah so Mr. Redford no longer stands out as an extremist (6) I 've already had a meeting uhm an update meeting so the place hasn't burnt down or anything We annotated those instances as CAUSE BELIEF, following the revised version of the PDTB 3.0 (PRAGMATIC CAUSE in PDTB 2.0).

for example, in our data we found frequent uses of pragmatic discourse relations, both epistemic and speechact, thus confirming the usefulness of the revised PDTB 3.0 hierarchy.


Looking at the categories that CCR distinguishes but the PDTB doesn't, it is revealed that the PDTB often does not distinguish between the source of coherence in its relation labels.

this dimension applies to causal, conditional and temporal relations.


We will sum over the corpus, how often these common subgraphs occur and how likely they can be mapped to each other based on the relations.

furthermore, we extend the scope of analysis and look for connected components common to both structures.


In our proposed annotation scheme, we mark not only the speakers and listeners but also their coreference chains.

the compatibility allows, on the one hand, direct exploitation of corpora where coreference is already annotated under these guidelines; it also facilitates, on the other hand, use of our corpus as coreference data.


For example, (i) a standard variant is used for writing communication which is well documented by dictionaries and grammars; (ii) these languages have standard orthographies and the majority of published texts adhere to these orthographic norms; (iii) large amounts of text are electronically available and can be used for developing NLP tools and resources.

in addition to results using the baseline approach two systems were trained using Phonetisaurus: • learning only from pairs corresponding to OOVs (pairs where the historical forms and normalized forms are different) • learning from all the pairs, including pairs where historical form and standard one are the same The results are similar to those we achieve in previous works on dialectal corpora.


Mann and Yarowsky (2001) documents a method for inducing translation lexicons based on transduction models of cognate pairs via bridge languages.

most of these characteristics are not shared by historical and dialectal text resources and, therefore, standard NLP tools can often not be directly applied to such corpora.


), various types of conversion, and a great heterogeneity as far as morphological orientation is concerned, cf.

most lexemes are connected to each other in both directions.


In addition, due to the semantic nature of PCs they almost exclusively occur with either an indefinite or definite determiner which precedes the non-head in English (and German).

the evaluation of the method showed that the accuracy of 76% could be improved by adding a step in the PC compounder module which specified user-defined contexts being sensitive to the part of speech of the non-head parts and by using treetagger, in line with our approach.


Given the differences among different DAs, this is also helpful for Arabic native speakers from different dialects.

many software tools were designed to help learners by providing dictionaries, expert feedback, carefully designed "immersive" experiences, etc.


The lemmas for which the second rule applies are difficult to identify in a list of over 50,000 entries.

the last part consists in transferring the database from old to new German spelling.


While the information about these special characters of a lemma can be easily extracted from the related entry in GOL, this is not always possible for the morphological analysis of the deepest morphological layer.

as it was developed in the Nineties, both encoding and spelling are outdated.


Indeed, the score associated to "bon" (good) might be different to the score associated to "bon" when it is modified by "pas" (not).

for the phrase "il a une très bonne odeur" (it has a very good smell), "très bonne odeur" (very good smell) is a target phrase rather than "bonne odeur" (good smell).


During annotation we achieved an interannotator agreement of 0.72 (Fleiss' κ).

for each approach the number applications and reviews used as well as the app store (Apple App Store (A), Google Play Store (G) or BlackBerry World (B)) they originate from are given.


In exact mode the predicted text spans of aspects and subjective phrases must exactly match those of the gold standard.

many reviews of the two categories focus on the accuracy (e.g.


The out-of-domain Subtask 3 had no participants.

the Museum domain had not been addressed in previous years or in other languages, so we had to compile a new set of guidelines for this domain.


4 The primary reasons for choosing this program were a) its non-commercial license, b) its portability to a wide variety of platforms, and c) a mature set of annotation features, such as possibility to create link attributes (used for coreference), mark overlapping elements, and assign multiple annotations of the same class to one token (which was heavily used by our experts for the cases when one sentiment statement was included into another opinion, e.g., She loves this ugly jacket).

working completely independently, one of the experts has annotated 78.8 percent of the full corpus, whereas another coder has labeled the complete dataset.


We also estimated the chance agreement p c in the usual way as: where c 1 and c 2 are the proportions of tokens annotated with the given class in the first and second annotations respectively, i.e., c 1 = A 1 T and c 2 = A 2 T .

we are currently testing our annotation guidelines on a new group of undergraduate students to see whether the provided descriptions generalize to other coders as well.


it overclassifies many tweets as opinionated).

a positive score of +2 and a negative score of -1 would have a combined score of +1.


This has been rarely addressed for sentiment analysis, due to the lack of available training data and the difficulty of manual annotation.

fair evaluation of such systems is fraught with difficulty, partly because the task is often subjective (human annotators do not agree on what is correct), and partly because comparing systems fairly is complicated when they tackle the problem in different ways.


This way, the machine learning model can also learn from tweets containing explicit expressions that do not include one of the tagged emotion word and implicit expressions of emotion.

such tweets have usually been excluded from existing gold standard corpora (Hasan, Rundensteiner, & Agu, 2014;Mohammad et al., 2014) to reduce complexity.


With the basic emotions (happiness, sadness, fear, anger, disgust and surprise) accepted as the state-of-the-art, existing emotion corpora and other language resources that serve as the basis for building and evaluating mechanisms to detect emotion in tweets are only annotated with those basic categories.

we then collected tweet streams using these 82 @usernames as the query terms from the Twitter API.


Note that since the multi-word phrases and single-word terms were drawn from a corpus of tweets, they include a small number of hashtag words (e.g., #wantit) and creatively spelled words (e.g., plssss).

a more natural annotation task for humans is to compare items (e.g., whether one word is more positive than the other).


2009) and for clustering we used agglomerative clustering, implemented in CluTo .

we learn from ConceptNet (Singh et al., 2002) properties of objects, actions and phenomena which cause certain emotions.


The precision of the obtained list at the level 1,000 first entries was estimated as 78.6%.

the participants should find solutions to overcome the lexical dependence on the training collection.


There are other studies in literature (e.g.

the idea of combining word embeddings and external knowledge to specialise them for a specific task has been actively explored in the last few years (Wang et al., 2014; Levy and 1 https://code.google.com/p/word2vec/ Goldberg, 2014;tang et al., 2014;Yu and Dredze, 2014;Wang et al., 2015;Pham et al., 2015;Liu et al., 2015), but all these studies extend the embeddings at word level.


In literature, there is a large bundle of studies for injecting some level of word order information into word embeddings (Qiu et al., 2014;Johnson and Zhang, 2015;Lai et al., 2015;Ling et al., 2015;Trask et al., 2015) or into general word-space models (Sahlgren et al., 2008;De Vine and Bruza, 2010;Basile et al., 2011).

see figure 3 for a schematic view of the order preserving model.


We propose to further extend PV models to insert such kind of information and specialise them for text polarity detection.

we refer to the work of (Trask et al., 2015) that extends the CBOW model proposing to split the context into two partitions, the first containing the sum of all the context word embeddings before the target word and the other containing the sum of all the word embeddings after.


In the experiment reported hereafter, these three properties (valence, arousal, domination) are interpreted as dimensions in a R 3 vector space.

we question this method by measuring the correlation between the sentiment proximity provided by opinion resources and the semantic similarity provided by word representations using different correlation coefficients.


There is comparable little work with respect to visualisation in lexical semantics: Kievit-Kylar and Jones (2012) present a tool that allows to identify relations between words in a graph structure, where words are visualised as nodes, and word similarities are shown as directed edges.

optionally, the user may provide text files with the gold standard and/or automatic class assignments, relying on two commaseparated columns per line: word, class .


 app.mikolov.models.overrideset it to true for overriding the default word models.

for instance, labeling aligned chunks with the underlying semantic relation type and computing semantic similarity scores for them would be extremely useful for explaining or interpreting why two texts are similar or dissimilar.


A chunk can have only one alignment and once aligned, it is not considered for further alignment.

at that time it only used gold chunks of the given text-pairs; these chunks were provided by the task organizers.


For each annotated reading, we then automatically collected morpho-syntactic and semantic features from the LVF.

abs Features relating to abstractness or concreteness.


The first set of features ('syn/sem' features) provides information on the morphosyntactic make-up of the verb, its argument structure and potential adjuncts, and indicates which suffixes are used in nominalisations and adjectives derived from the verb, when those are available (note that derived nouns and adjectives are coupled to a particular reading only if they preserve the meaning of the verb on this particular reading).

weak accomplishment readings are primarily telic, but nevertheless share some properties with activity readings (see Piñón (2006)).


On one hand, these were manually assigned gold aspectual values on the telicity scale of eight values (as described in the previous section).

in this work, we focus on the way the lexical aspect of a same verb varies across its different readings, using the lexical resource "Les Verbes Français" (François et al.


There are only 16 unambiguous verbs, while the largest number of readings is 37 and 19 (for 1 respectively 2 lemmas).

features indicative of concreteness are characteristic both of the VARiable and TELic classes.


The conditioning of sparsity on the maximum activation area G max i .

two distinct models are built, namely, domain and function spaces.


For instance, for an application involving a simple interface to a robot, quantifiers may not be needed.

we describe resources aimed at increasing the usability of the semantic representations utilized within the DELPH-IN (Deep Linguistic Processing with HPSG) consortium.


How to Lose Weight is a title of a document which contains 5 step titles, including Keep your own personal food diary and determine your weight loss goals upfront, Have a balanced diet, Avoid skipping meals, Eat food from home, and Learn to love fruit.

the results show (1) 20.66% of the recommended subtasks appear in wikiHow, (2) 28.98% of the steps in wikiHow appear in our recommendation set, and (3) 47.04% of the recommended subtasks not appearing in wikiHow are labelled as correct by human assessors.


Indeed, semantic vectors built for a single language were able to predict in an unsupervised setting typological patterns that hold across languages.

all models produced statistically significant correlation with typological data (p < 0.01).


The list of contexts includes nouns presupposing both direct and figurative usages of the adjective in question (e.g.

it has been argued that patterns like 'sharp'→'spicy' are not arbitrary but are motivated by an inherent semantic basis, which is common for different languages (Rakhilina and Reznikova, 2013;Koptjevskaja-Tamm et al., 2015).


Consider the following example: RT @NewAppleDevice: Apple's smartwatch can be a games platform and here's why http://t.co/uIMGDyw08I It contains factual information that can be understood even without visiting the link.

given the 4000 tweets on the four topics mentioned above, Table 1 reports on the number of tweets annotated as arguments.


if tweets contain pronouns only (preventing the understanding of the text), we consider such tweets as not "self-contained" , and thus non arguments.


Extended syntactic and semantic information was added to the lexicon, in order to formalize and process them.

the module can be downloaded from the NooJ website 2 .


We believe that this is due to the composition of the feature set we use.

this does not reflect the reality in forensic scenarios: in practical linguistic forensic investigations, the resources that are available to profile the author of a text are usually scarce.


As table 6 is sorted in order of decreasing positive training instances available for a research topic, we observe that topics with more positive training instances tend to be more lossy.

we investigated the task of classifying funded grants descriptions into research topics.


Automatic assignments are useful for decision making only if they are very precise.

initiative, holding institution and department (table 1), were sentence split and tokenised.


We employ separate views for the free text fields, the award details and the principal investigator's name.

as shown in table 8 we computed average scores for research topics for which the 5 agreements method achieved an F-score higher than 75%, i.e.


Collaboration among editors with different skills is essential to developing high quality articles (Kittur and Kraut, 2008).

in detail, we used two of the multi-label classifier implemented in Mulan (Tsoumakas et al., 2011) with ten cross validation.


There are three main future particles in YEMS: š, ς, y.

• POS5 is a reduced tag set of five tags based on traditional Arabic grammar.


Moreover, while the word grammar in Humor describes productive Hungarian compound constructions quite accurately, the description of compounds in the morphdb.hu grammar is extremely simple: it allows an arbitrary number and type of nominal stems (nouns, adjectives or numerals) to occur in a compound in an arbitrary order (accepting for example the non-existing tevepiroshatvannegyven, 'camelredsixtyforty' as a correct compound), this tool returned much more nonsense analyses with the productive compounding function on than the other two analyzers.


These annotations are then used to compute the gold standard using different methods, and a series of regression experiments is conducted to evaluate their impact on the performance of a regression model predicting the degree of naturalness of L2 speech.

additionally, to add some more specialised (but still text-independent features) we also compute duration, rhythm, and prosodic features derived from a segmentation of the recordings into vocalic and consonantal intervals, and from pseudo-syllables derived from that segmentation.


When we employ the default cross-validation procedures of toolboxes such as WEKA or sklearn, we obtain (with 10 folds and late fusion of all six feature groups) ρ=0.715.

these features were computed using the segmentation of pauses, vowels, consonants, and speaker noise derived from the PR.


In French, fricatives and plosives at the end of words can also occur as voiced consonants -in contrast to German where final devoicing applies.

it provides spoken data by many speakers with comparable productions, annotated and segmented on the word and the phone level, with a substantial amount of hand-correction.


Of course, in this case, general frequency effects and the diversity of situations of the data collection (which always corresponded to naturalistic settings) constrain the results.

for instance, "colar" is ambiguous given that it may correspond to the common noun 'necklace' (in which case the word should be tagged as "CN|colar") or to the infinitive form of the verb 'to glue' (in which case it should be tagged as "V|colar").


In the worst cases, which is the case for the majority of the 6000+ languages spoken in the world, no computational lexicons are available 1 .

since the multilingual Wikipedia project encourages the production of encyclopedic-like articles in many world languages, we can find there an ever-growing source of text from which to extract these two language modelling elements: word list and frequency.


Place names are often given with their original language names; national anthems can be listed with their original language lyrics, as well as translations; book, song and  ' Anarchism ' is a political philosophy that advocates stateless societies , often defined as self-governed , voluntary institutions , but that several authors have defined as more specific institutions based on non-hierarchical free associations .

a useful first step for under-resourced would be to provide at least a list of known words in that language.


In recent years, several datasets have been released that include images and text, giving impulse to new methods that combine natural language processing and computer vision.

the data are stored as one JSON-LD file per news website.


The corpus consists not only of words but also compounds and phrases.

these corpora often contain few users, but a higher number of repetitions per user to improve recognition performance (Cooper et al., 2011).


To record realistic data (but not necessarily played by elder person), people under 60 were recruited.

cIRDO may operate a transfer of the risk recognition: It is the technology that gives official status to a fall in a domestic incident.


There are a number of limiting factors, among them standardization and difficulties in manual annotation.

among them, 637 types have been recognized as conventional lexical units and have become part of the lexical database.


Due to organisatory reasons five teachers explained the task to a 'knowing' learner, who was already acquainted with the task but instructed to act as if he/she did not know the task.


The learner is only observing while the teacher is explaining and conducting the task.

further tiers with information derived from recording force (Task 2) and motion data (Tasks 1-4) will be annotated.


a cluster which contains segments with different labels is divided.


We also claim that it is feasible to learn a certain set of rules, e.g.

variations within the "complete" segments can be considered as naturally occurring variants that need to be captured by a learning model.


After a short introduction the investigator hands over a list of tasks to the participant and leaves the room.

by adding logical or semantic information to our database learning could be enhanced.


(2012) talk about sign language corpora of videos, and present a project that aims to translate visual sign language to written spoken language, with a written sign language middle step.

as it is common in sign languages, some words in spoken languages have no sign.


The IMAGACT data accounts for the semantic competence, separating the contexts from the metaphorical and the idiomatic expressions.

the animated videos as demonstrated in Figure 2 illustrated on the IMAGACt platform exhibit some ambiguity with reference to different types of actions.


We use DBpedia as a supervisory tool to guide the clustering process, so that the structure of learned ontology is more similar to Wikipedia concept hierarchy.

we cluster different kind of relations between terms into groups.


Ultimately, it is the need of the users that decide which degree of coverage is acceptable.

it should also be offered to teachers and authors of text books for higher education.


We therefore added a Difference measure, which shows the difference between the coverage for each list in the two corpora.

the choice between them was decided by testing coverage (see Section 5).


This means that a lot of academic words have been lost compared with the G&D-2.6_0.3_0.6_3.2 list, which has 6.74 in KIAP.


In the case of the example sentence 'Ford hires John as their new CEO', the instantiation of the defined situations for the event instance of eso:JoiningAnOrganization will then look as follows: :eventX_pre { :John eso:notEmployedAt :Ford :eventX_post :John eso:employedAt :Ford :John eso:hasFunction :new CEO :John eso:isEmployed : 'true' } Instantiation of events that express a change in a scalar value By default, situation assertions will only fire if some instance for an ESO role is found by the SRL module.

in total, 268 events were found with inferred pre and post situations and 47 events with inferred during situations.


Frames that denote finegrained semantic distinctions are often grouped into one class in ESO since these distinctions do not influence the modeling of a salient set of pre and post situations.

lexical structures do not make explicit how the meaning of a verb needs to be combined with other event components, such as the participants and the temporal properties for the purpose of semantic parsing.


In order to guarantee a fair comparison with UT-based approaches, we only consider triples assigning word classes (subclasses of olia:MorphosyntacticCategory, roughly corresponding to UD POS tags), but ignore triples with olia:MorphosyntacticFeature assignments (roughly corresponding to morphosyntatic features in UD).

our evaluation is biased towards features from the German annotation as we use the German-based MLG gold annotations for bootstrapping the annotation scheme.


For w i , we can expect that this will be true for more than one category.

for each of them we find three columns: "Ok" for the number of correct cases, "Total" for the total number of cases and "P" for the precision as the ratio between both values.


We used the text of Spanish Wikipedia as a corpus, not because we have a particular interest on it, but because it is large and freely available.

of the application of this first step, the CPA ontology, which initially contained a set of 250 semantic types, has grown to a total of 2,290 categories, each one representing a parent node populated with hyponyms.


The POS tagging of the GOLD standard is relevant in two ways for this project: on one hand, we expect POS tags to provide useful information for the (manual or partly automatic) segmentation process.

ultimately, segmentation with 0.3 seconds pauses as a segment boundary delivered the best results for learners' speech.


All these phenomena are marked with dummy placeholders in the normalization process and are therefore easy to filter out in the training process and easily tagged in a post-processing.

we expect the performance of POS taggers to improve once the data has been segmented into units comparable to the sentences of written language (see above).


AMIRA uses punctuation as an indicator for a new token so replies, mentions, retweets and hashtags in tweets are broken into the indicator part (@ for replies, mentions and retweets and # for hashtags) and the remainder of them.

there is a need for a POS tagger which should take into consideration the characteristics of Arabic tweets and yield acceptable results.


These findings hint at the need of further developing taggers possibly by extending the feature space (e.g., by morphological, syntactic or even semantic features).

extracting lemmas and syntactic words (including all grammatical categories available) from a Wiktionary instance in a thorough and robust way is not a trivial task.


We presented TLT-CRF as a hybrid morphological tagger for Latin which employs lexicon-based features as well as hand-crafted rules in the framework of 1st order CRFs.

beyond Part of Speech (PoS), TLT-CRF tags eight inflectional categories of verbs, adjectives or nouns.


it uses the lexicon-as-features rather than the lexicon-as-constraint approach.


it uses the lexicon-as-features rather than the lexicon-as-constraint approach.

during the split of the corpus we did not shuffle sentences, but documents, thereby obtaining a more realistic split of the data.


We test our tagger on Slovene data, obtaining a 25% error reduction of the best previous results both on known and unknown words.

to most other taggers of highly inflected languages, tagging of unknown words is directly incorporated into the tagger architecture, rather than being handled by an additional process.


For example, we can understand the word mới in accordance with two meanings shown in rows 1 and 2 of Table 2.

the quantifier indicating the whole will be annotated with a Pp tag if it is head word of a noun phrase.


The example is given in row 3 of Table 1.

we then discuss difficulties in wS, POS tagging, and bracketing, and how we solve them in developing the annotation guideline in Section 3, 4, and 5 respectively.


For example, khó_khăn can be understood as difficulty or difficult.

(2009) classified the words on the basis of their combination ability and syntactic function.


Determining POS of the words having the same surface form may be more ambiguous because a verb or an adjective can appear in the position of a noun as in the case of báo cáo in rows 3 and 4 of  (as the case of báo cáo in rows 5 6 of Table 2).

in this section, we will discuss our method to improve the quality of annotation guidelines and to ensure correct and consistent annotation.


Words that have the same surface form and pronunciation but different meanings and grammar functions occur frequently in the text.

to achieve a high agreement ratio, the annotators need to be trained carefully and to practice the annotation more on the basis of real texts so that they become familiar with annotation and analyzing the texts following the guidelines; the guidelines also need to be updated for new constructions throughout the annotation process.


a pattern with k nodes is called k-pattern.


As the result, the method fails to correct the annotation error in (c).

t rans(τ ′ , τ t ) does not hold for any τ ′ and τ t .


The constituents of the cleft construction são.

named entities), or is composed of relations between words (e.g.


The comparison also shows that written Slovenian is significantly more nominal than spoken Slovenian with a higher share of nominal and prepositional phrases (the nmod and case relations) that are also more complex in terms of the number of adjectival (amod) or clausal (acl) attributives.

a detailed description of the sampled treebank, currently amounting to 3,188 utterances or 29,468 tokens, is given in   Typically, spoken language annotation denotes annotation of its representation in the form of written transcription.


The first version of the Spoken Slovenian Treebank is planned to be released as part of the UD release v1.3 under the CC-BY-NC-SA 4.0 licence.

the large majority of texts in the treebank include longer continuous spans of complete turns by one or more speakers, enabling posterior extension, re-segmentation or addition of other layers of linguistic annotation, such as discourse relations annotation or dialogue act annotation.


This paper is organized as follows: Section 2. gives an overview of the design of the ALT corpus.

for example, the three nodes "(/PUNC'', "1/NUM" and ")/PUNC" form the phrase "(1)/NUM''.


The conversion is implemented as a cascade of structural rules followed by a relation conversion procedure over the modified graph structures.

all experiments are performed on the data splits detailed in Table 6.


NDT implements a somewhat broader definition of prepositions than the UD scheme and includes several cases which are counted as adverbs in the universal schemes.

the notion of control is not native to NDt, hence we approximate it by requiring the presence of an explicit subject dependent of the head verb of the clause.


Without a gold standard for Norwegian UD dependencies, it is difficult to evaluate our conversion directly.

this is the case for demonstrative adverbs such as her 'here' and der 'there'.


a truly multilingual Europe, which is supported through sophisticated Language Technologies (LT) is still far from being a reality.


A certain amount of community building has indeed successfully taken place, also due to META-NET activities such as the META-FORUM conference series.

as well as data, tools and technologies distribution through open repositories.


An essential step is the preparation of a convincing, realistic, yet visionary research and innovation strategy that includes solutions for the Multilingual Digital Single Market.

to non-initiated outsiders, the wider community around the Multilingual Europe topic still appears to be very much fragmented.


It seems that the more a question requires self-commitment, or to give an opinion, the higher the ratio of "I do not want to answer".

finally, our proposal of a special issue of the TAL journal on Ethics and NLP has been accepted and a workshop dedicated to ethics, ETHI-CA 2 (ETHics In Corpus collection, Annotation and Application) will be held during LREC 2016.


Again, out of the 200 participants, 73 people declared that they are interested in participating in a working group on ethics in NLP (31 gave their Email), which is very encouraging (see Table 1).

it seems that our question on whistle blowing ("Do you know of any way to blow the whistle (issue an alert) in case of a major ethical infringement in your domain?")


Decentralised publication, however, has been shown by the linked open data community to support massive scaling in data exchange (Bizer et al 2009).

used when human intervention is required to review the Policy.


An example of an accepted LR is presented in   Since its launching, the ISLRN identification number has been adopted by large institutions over the world.


The rise of speech-dependent customer service centers and mobile phone applications, as well as the growing need for better speech synthesis systems (e.g., in-car navigation tools) is reflected in industry reports as well as at conferences featuring industry solutions, like SpeechTEK.

twice each year students apply by submitting a Data Use Statement and Letter of Support from their advisor.


This is unfortunate for reasons of comparison.

we present a novel corpus of tweets annotated with the MBTI personality type and gender of their author for six western European languages (Dutch, German, French, Italian, Portuguese and Spanish).


It is important to mention here that all the previous large scale annotation studies known to us focused only on Level 4 (language and POS at word level).

en Trans: drkumarvishwas had said during victory celebration after anna fast that: let the janlokpal pass there will be such a celebration that the world will see.


In the current study, we analyzed CS data from Twitter for pragmatic or functional aspects (Barredo, 1997;Sanchez, 1983;Maschler, 1991;Blom and Gumprez, 1972).

the nature and extent of CS depends on a number of factors, including structural, pragmatic (functional), and socio-cultural aspects.


But we knew that this list was neither exhaustive nor necessarily suitable for Twitter.

an analysis of CS data requires inputs at Social, Contextual, and different linguistic and meta-linguistic levels that operate on various sub-parts of the conversation.


Dependency types rely on the definition of a "clause," such as the distinction between nsubj and csubj.

for a relative clause, the head of the dependency is the noun modified by the clause; the dependent is the main predicate of the clause as shown in (8).


It is possible to argue that these constructions share the same dependency type.

7 this type is also used for fronted or postposed elements that do not fulfill the usual core grammatical relations; for example, the relation between "office" and "me" in the sentence "This is our office, me and Sam."


These tools allow querying any of the UD treebanks, freely combining restrictions on the existence or absence of wordforms, lemmas, POS tags, morphological features, dependency labels, and subtrees.

figure 2 uses the french sentence Toutefois, les filles adorent les desserts au chocolat (the girls love chocolate desserts) to exemplify the different UD annotation layers, which are described in more detail in the following sections.


An issue that we did not consider so far are revisions to the first stages of the pipeline, namely tokenization, sentence splitting, POS tagging and the generation of morphological features.

(iii) Can we avoid any loss of linguistic information and loss of accuracy in the process?


In order to support automatic conversion into UD, the MIDT Treebank was extended with extra information (we call the enhanced version MIDT+).

mIDT in turn was obtained by merging two existing Italian treebanks, with different annotation schemes: TUT, the Turin University Treebank (Bosco et al.


In such a way, we take advantage of the graph's properties, such as, the neighborhood, the edge weights, the size.

the most frequent extracted features from datasets, are frequency, mean, standard deviation, etc.


We next describe our corpus collection and annotation in Section 3, and present the general idea of our system in Section 4.

instead of calling them best and mode, we thus report best, best 3 and mode 3.


These papers are already provided in a clean, hand-authored XML format with a well-defined XML schema 3 .

we integrate automatic Core Scientific Concepts (CoreSC) classification into a prototype context-based citation recommendation system and investigate its usefulness to the task.


We attempt to capture the possibility of Arg2 being present in that position if the connective was playing the role of a discourse connective.

begum, R., Husain, S., Dhwaj, A., Sharma, D. M., bai, L., and Sangal, R. (2008).


Unlike Arg2, which is always occurs after the connective, there is no such constraint for Arg1.

a fully automated discourse analyzer would thus greatly aid discourse analysis and NLP applications such as text summarization and question answering.


To keep a changing data consistent, especially during an annotation phase, a well designed annotation format does not contain redundancies.

processed with output filters to produce statistical tables.


Such clauses are represented by a node with the syntactic category SINV in the PTB annotation.

to make queries in the PML-TQ simpler, we have added several redundancies to the transformed data of the PDTB-2.0, as the last step just before the transformation of the data into the database format.


The tags 'Marking Aside', 'Adding to topic' and 'Managing Audience Discipline' were excluded from the final set of MD tags, due to their low occurrences in the trial study.

different instructions sets have been designed for each of the 19 MD category in the schema.


Leading discourse markers such as "ok", "right" and "so" are regarded as individual utterances (unless "so" can be replaced by "hence").

x-turns tier containing the turn boundaries for participant x x-utts tier used for segmentation and transcription of x's utterances x-en tier containing English paraphrase translation for x's utterances x-laughter tier containing the laughter and laughed speech duration intervals for participant x Segmentation: In the x-turns tiers, all continuous stretches of speech by one speaker until the other speaker takes over, modulo small overlaps, are considered one turn.


Establishing reliable storyline clustering benchmarking data sets and metrics is one of the goals of the SUMMA project, as good storyline clusters are the prerequisite for downstream storyline summarization, visualization, and predictive anticipation of upcoming developments.

to enable joint multi-task learning for multilingual neural translation of morphologically rich languages we replace the attention mechanism with the sliding-window mechanism and operate the sequence to sequence neural translation model on the character-level rather than on the word-level.


It is still an open issue which vectorspace projections yield the semantically best clusters (Hill, Cho & Korhonen, 2016) and further experiments are needed.

this is an order of magnitude slower than regular NEL or IF IDF bagof-words based clustering methods.


The approach is informed by our previous work on machine learning (Barzdins, Paikens, Gosko, 2013), media monitoring (Barzdins et al.,2014), and character-level neural translation (Barzdins & Gosko, 2016).

for storyline (Rissen et al., 2013) clustering the signals for the stories belonging to the same storyline might be not so much the semantic similarity of the articles (they might report various developments of the storyline from differing viewpoints), but rather the matching time and location as well as same organizations and people being involved -the information typically supplied by Named Entity Linking (NEL) tools.


Finally, we calculated the inter-annotator agreement for the identification of contrasting evaluations.

3/9/2016 brat ¶ @afneil @daily_politics @TheSunNewspaper Missed off the #irony hashtag?


8 For Brazilians, these words were probably an indication that the text was not written in Brazilian Portuguese.

(2015) showed that for 52 misclassified Portuguese instances, only 22 have been labeled correctly by the annotators with low inter-annotator agreement.


This is essentially NRC's first 2015 run, which performed 0.7% below the top system, trained using various training set sizes.

in addition to the full 2014 and 2015 test sets, we evaluated performance on the subset of the 2015 test set that contains the groups in the 2014 shared task, i.e.


Scanned PDF files were also excluded since there is no sufficiently accurate Arabic Optical Character Recognition (OCR) system for the low resolution files we came across; although most of the OCRs available online claim high accuracy, tests reveal that their output needs editing (Abbas, Atwell and Al-Sulaiti 2016).

the 'Create Corpus' tool in SketchEngine (Kilgarriff et al 2014) enables researchers to gather texts in a range of different formats and then upload them into SketchEngine format.


Based on a ceiling of 30,000 out of approximately 800,000, the duplication ratio is at most 3.375% in RCV1.

the concept of information entropy was defined by Shannon in his seminal text (1948).


The paper has a substantial number of corrections which can be applied to the corpus; their revision is referred to as RCV1-v2.

the result from an experiment with an ideal 100% accurate classifier would be reported as something like 99.5% because of classification errors in the corpus -thus, it is not implausible to think that a less accurate classifier could win an evaluation.


• Expenses are often referred to as an alternative to Costs.

and instead marked such a statement as FL, for 'forward-looking', under Sent_Tone.


In the Accounting and Finance literature, there is an increasing body of work using basic word-list and ML approaches to examine the information content of forward-looking statements (e.g., Li (Li, 2010)) and other work has looked at the relationship of optimistic versus mild statements in the context of positive and negative financial performance (Chen et al., 2013).

we also performed conceptual anno-tation where we counted the frequency of semantic (Sem) tags A2.1 (Affect:-'Modify', 'Change') and A2.2 (Affect:-'Cause'/'Connected') those believed by the accounting and finance expert to be good indicators of attribution.


We collected 500 Preliminary Earning Announcements (PEAs) released between 2010 and 2012 by firms listed on the London Stock Exchange.

another coder might well decide to make the ambiguity inherent in the statement explicit by marking this sentence as UNSURE, EXT, UNSURE.


The idea is the following: the more P (c j |t) is different from P (c j ), the more important the term t i is.

recalculate the distances between new cluster and other clusters.


Overall, we tried to create near realistic plagiarism settings.

a passage of size between 200 -300 words was excerpted from each source Wikipedia article.


3 Resolution of request/problem A message that aims to provide a resolution to a previously submitted request or problem report.

the idea of identifying requests and responses within online communication has been applied to email (Shrestha and McKeown, 2004;Carvalho and Cohen, 2005;Lampert et al., 2008), online forums (Ding et al., 2008;Cong et al., 2008) and online forums and issue trackers related to OSS (Korkontzelos and Ananiadou, 2014).


Evaluation was per-  Table 7: Evaluation results for the multi-label setting.

deciding whether to adopt an OSS project requires an assessment of its quality at the time of adoption and possibly an estimate of the perceived enthusiasm of the community towards it in the future.


L2=L3 Others Percentage (%) 27.47 44.27 28.27 Table 6 Noisy Labels Distribution In this paper, we present a method that combines automatic selection and manual annotation selection method based on natural hashtag labels to construct an emotion analysis corpus.

the author feels sad because he will leave someone because of holiday and the lexicon-based method set it as sadness because of the word "miss".


Hence, when combining several utterances, an utterance's matrix language and the matrix language of the previous utterance need to be represented (if they differ, that implies adding a code-alternation point between the two utterances).

it is also shown how this formal measure can be used in practice, by applying it to several code-switched corpora.


2015, and the four corpora 6 used in the shared task on word-level language detection in code-switched text (Solorio et al., 2014) organized by the workshop on Computational Approaches to Code Switching at the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).

so does the annotation strategy chosen for the EMNLP corpora prescribe that elements such as abbreviations should be tagged with the language they belong to, while other annotations schemes treat them as language independent.


For this reason, the dialectal Arabic corpus actually was the one causing most problems for the processors in the EMNLP 2014 shared task on code-switched language identification.

code-switching is most prominent in spoken language conversations and has thus traditionally mainly been studied by psycho-and sociolinguists (Auer, 1999;Muysken, 2000;Gafaranga and Torras, 2002;Bullock et al., 2014) and by speech researchers (Lyu et al., 2015), while the lack of large-scale textual corpora has made code-switching less attractive as a subject of study in computational or corpora linguistics.


If an utterance x only contains language independent tokens, its code-mixing is zero; for other utterances, the level of mixing depends on the fraction of language dependent tokens that belong to the matrix language (the most frequent language in the utterance) and on N , the number of tokens in x except the language independent ones (i.e., all tokens that belong to any language This initial measure has several short-comings.

they were only interested in the relative difficulty and give no measure of the complexity as such.


It is installed in our main lecture hall (Audimax) and in multiple other lecture halls.

although an archive might be useful for later studying and the preparation for the final exam, the live function allows for more situational context information.


In the summer term, an active usage of the LT in all seven lectures could be observed.

it was also a possibility to get for more detailed answers.


This release of the corpus consists of 300 abstracts from articles in the ACL Anthology Reference Corpus, published between 1978-2006.

we are grateful to Prof. Elke Teich and Prof. Laura Kallmeyer for supporting development and dissemination of this resource.


Independently of each other, we annotated these abstracts.

this is often the case in anaphoric contexts (e.g., as in ".


In recent years, machine translation (MT) became widely used by translation companies to reduce their costs and improve their speed.

the corpus includes 520 articles with an average of 192 words per article.


We believe that this battery of evaluation techniques is necessary for a principled machine translation development and to understand the nature of the output.

although the results are not comparable across the language pairs, given the different nature of the languages involved, they are indicative of the amount of work to be done.


The numbers of Accuracy and Fluency errors are quite similar, with slightly more Fluency than Accuracy errors.

a full MT development process often relies on a mix of automatic measures and human rating.


As our measures show, self-plagiarism is a common We'd like to thank Wolfgang Hess for the ISCA archive, Douglas O'Shaughnessy, Denise Hurley, Rebecca Wollman and Casey Schwartz for the IEEE data, Nicoletta Calzolari, Helen van der Stelt and Jolanda Voogd for the LRE Journal articles, Olivier Hamon and Khalid Choukri for the LREC proceedings, Nicoletta Calzolari, Irene Russo, Riccardo Del Gratta, Khalid Choukri for the LRE Map, Min-Yen Kan for the ACL Anthology, Florian Boudin for the TALN proceedings and Ellen Voorhees for the TREC proceedings.

when we look at the five top sources (marked in the extreme right column), we see that the main sources of "inspiration" for LREC papers are ISCA and LREC itself.


The majority (90%) of the documents come from conferences, the rest coming from journals.

"British National Corpus" is considered as possibly abbreviated to "BNC", as well as less regular names like "ItalWordNet" possibly abbreviated to "IWN".


Document expansion, in addition to inserting text and hyperlinks, can also involve adding non textual content such as images that are topically related to document text, in order to enhance the readability (or in fact indexability) of the text.

we demonstrate that a tf-idf based term weighting for the query terms can prove useful in improving retrieval effectiveness, thus leaving open the future directions of research for effective query representation for this task.


These methods are useful in order to arrive at an overview of the content of large corpora.

several technologies can detect related elements in texts, and the predicate that indicates their relation.


Two rules were created to deal with this non-standard pronoun use, and anaphora resolution was limited to cases covered by these rules:  An actor in the subject of a sentence's main verb (based on dependency parsing) is taken as the antecedent of a sentence-initial he/she in the following sentence.

the ENB-IPCC test-set does not come from the same corpus that the system was developed for, and serves as a way to test the system on texts from a slightly different domain, in terms of syntactic structures and lexical items involved.


In the open RE task there is a great diversity of relations, which makes their classification more difficult.

the produced resources related to this paper (the subset of texts and corresponding positive relation instances manually annotated in tuples (NE1, relation descriptor, NE2) used in each experiment) are electronically available at: http://www.inf.pucrs.br/linatural/data set RE.html In future work, we plan to use open source tools; as well as performing an extension of the proposed process for other languages.


Since each Web page is retrieved by AND search of the given query q j and the search engine suggest s, one or more suggests are assigned to each Web page.

those three statistics have quite high correlations.


JAPAN Big Data Report 5 , search queries that have high correlation with economic boom as well as economic depression are identified.

then, from the set D N (z k , q j ), we collect Web pages which satisfy the lower bound θ lbd into the set D N (z k , q j , θ lbd ) as below: Furthermore, we collect search engine suggests assigned to each Web page d in the set D N (z k , q j , θ lbd ) into the set S(z n , q j , N, θ lbd ) as below: For each topic z n , we collect search engine suggests for a query q j (j = 1, .


We finally propose how to predict the market share of a specific product genre based  We examine 10 query company names "ASUS", "Lenovo", "NEC", "SONY", " " (SHARP), " " (Panasonic), " " (MITSUBISHI ELECTRIC), " " (FUJITSU), " " (HITACHI), and " " (TOSHIBA).

it has become popular recently to analyze concerns of those who search for Web pages and to compare them with people's behavior in real world.


(2010) were not significant enough to influence automatic evaluation in recent work.

this leads to overly pessimistic scores since variations in the extracted keyphrases that might be judged as correct cannot be taken into account (Zesch and Gurevych, 2009).


We assume that due to specialization, scientific texts exhibit greater encoding density over time, i.e.

to build such corpora, usually high manual effort is involved.


a preliminary experiment showed that basic Levenshtein algorithm produces 1.1 % of errors, that is almost 12,000 sounds for our 30 hours of experimental speech data.


The conversation was natural, the range of topics was not limited in any way.

in the read texts pauses were labelled automatically, with manual correction where necessary; additionally, when the speaker had problems with reading some words or phrases and re-read them, the defective fragment was segmented and labelled as a false-start.


Furthermore, placing two strong stresses within one orthographic word was not forbidden, since it is quite possible in colloquial speech.

due to very frequent clipping in some speakers' recordings, defective fragments shorter than 100 ms were allowed.


We collect tons of news from mainstream news websites and calculate the co-occurrence of Bollywood artists' names in these articles, which is a plausible profound and comprehensive way to tell the relativeness of two singers.

the same humming query is used in both figures with different scaling and the main melody.


We propose a query by humming system for music retrieval that uses deep neural networks for note transcription and a note-based retrieval system for retrieving the correct song from the database.

the humming query input is generally divided into several segments and linear scaling is used on each of the segment to get the optimal distance between the query   The candidate songs are then ranked by their smallest distance with the query, with the song with the lowest distance ranked first.


At the output of the Speech/Non-speech Discrimination module, the specificity is high, Sp = 0.996, this parameter represents the classification rate of non-speech events classified as non-speech.

this model is unigram and contains 11018 words.


Among the 1950 audio events, 277 are distress calls, 45 are colloquial sentences and 1628 are non-speech.

the speech technology, though mature is still difficult to master for non-expert and to tune for a particular usage (Vacher et al., 2015a).


Taking a closer look at the random fit for each transcriber ( Figure 5) reveals that the poor performance of FS − was largely attributed to the non-expert group CROWD, who were not able to beat the ASR output with this method.

in addition, there has also been a rise in computer-assisted transcription, in which automatic transcription is used as a starting point and human intervention is only needed when the ASR produces errors (Rodriguez et al., 2007).


As mentioned in §2., each participant's audio is recorded with a Microcone microphone array in a real environment.

in the offline experiments, on the other hand, the test dataset has been well defined.


This may lead to disturbances in any of the components involved in the speech production, including respiratory, phonatory, resonatory, articulatory and prosodic elements.

we can point out that the spontaneous speech productions are shorter (duration and # of phones) than the read speech productions independently of pathologies.


In order to account for phonemic effects such coarticulation the monophones were expanded using the full linguistic context.

without these errors the overall token-level accuracy would be 90.5%, which means that about half of the token-level errors are due to these difficult aspects of Bangla orthography and phonology.


It is based on the simple hypothesis that states that during a TV newscast, when the name of a person appears on screen, that person is presently speaking.

once the speech tracks identified, the LIUM speaker diarization tool (Rouvier et al., 2013) is used to provide a speaker segmentation and clustering of the analyzed shows.


For the sake of comparison, we consider that a message in chats corresponds to a breath group on the phone.


This can be used to identify words' variations or misspellings (Kondrak and Dorr, 2004).

we also showed that the use of post-processing patterns would not lead to a substantial increase but would be useful to balance the precision/recall ratio.


The participation rate (19%) of the group members for the collaborative translation was rather high, and produced a decent number of viable translations that allow for interesting insights into the use of dialect on the Internet.

for our stop words list, we used a publicly available subcorpus of the DCK, which is called the "release corpus" 5 .


Bavaria is one of 16 federal states in Germany.

most of the visits occurred during the first 4 days; the whole experiment lasted for 10 days.


This is probably from the period when Wikipedia had not enforced the format of editors' signatures when they were making posts.

our corpus represents a considerably large number of interactions between the same set of people.


For the identification of the NE categories Person, Organization and Place, we use the NERP-CRF output described in Section 3.2.. After, we identify the first pair of NEs in each sentence.

the authors acknowledge the financial support of CNPq (Conselho Nacional de Desenvolvimento Científico e tecnológico), CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) and FAPERGS (Fundação de Amparo à Pesquisa do Rio Grande do Sul).


In (a) the noun phrases "o agrônomo " "the agronomist " and "Miguel Guerra" " Guerra" are considered coreferent, in other words, they belong to the same coreference chain.

there were 1,086 NEs identified, distributed in the ten HAREM categories.


The original annotation guidelines have been created and tested for the first release of ARRAU (Poesio and Artstein, 2008).

as a part of our ongoing work, we are analyzing the outcome of our efforts, in particular, of the measures undertaken to improve the data quality.


Again, it is very important to have the same corpus annotated for both bridging and coreference since these two tasks are very interrelated.

we have doubled the corpus size, expanded the selection of covered phenomena to include referentiality and genericity and designed and implemented a methodology for enforcing the consistency of the manual annotation.


Presumably not, leading us to suppose that elaborations of our annotation in which distinct idioms are distinguished would be relevant.

w i represents the pool of spatial skip bigrams within the ith segment.


The semantic relation we consider is relatedness of terms in thesauri for intellectual document classification.

both for Eurovoc-1 and Eurovoc-2 we only selected terms consisting of one word and we moreover required that word occurs at least once in the UKWaC.


In 5 the arrival event is annotated as POS as it may still occur (conditioned on the accomplishment of the if clause).

they have annotated a corpus and have worked on the automatic detection of factuality.


For each word, some boolean attributes indicate if they belong to some of the lexical items lists.

an implicative verb (-+), such as dudar/hesitate, must have negative polarity to imply that events in its scope are factual (Juan no dudó en abrir la puerta / John didn`t hesitate to open the door: dudar has negative polarity, so abrir is R).


The objective of this model is to predict: where w j are words, represented as one-hot vectors in the model.

some user wanting to use the framework is obligated to learn how Theano works too, which can be time costly.


Depending on the context and the speed of elocution, these inflections may correspond to a pause, which implies a silence and, normally, the end of the sentence, or a semi-pause, which implies an intonation change in the same sentence.

the CHILDES database (MacWhinney, 2000) or the Alborada corpus (Saz et al., 2010) have recordings of people with different typologies of intellectual disability, but, these corpora don't focus on prosody.


The ultimate goal of this project is dissemination of a valuable resource to the HLT community.

third, linguistic data gathered from natural language samples could help researchers in their quest to understand the biological basis of ASD.


Apart from this, using a multiple token title as a label predictor for a single token requires to solve questions concerning overlap.

these phenomena form the theoretical foundation of a noun bias for titles in the Wikipedia.


From a typological point of view, due to availability and other factors, there is a danger of overfitting to Indo-European languages written in the Latin script.

we extracted the nouns, verbs and adjectives for the respective languages present in the English version of the wiktionary, which has entries for almost every language.


This is because few available resources include a wide range of word-color associations.

ideally, such words would have one association for each color because the participants would arbitrarily select one color from the 11 basic colors.


We were not able to confirm that the participants did not simply choose color associations in a random fashion.

some words are associated with colors (i.e., danger and red) and these associations play important roles in information visualization.


In distributional semantics, the meaning of a word is a function of the contexts in which that word occurs (Turney et al., 2010).

these associations do tell us what the recordings sound like.


To perform this disambiguation, we show annotators a list of sentences each one containing the target verb in one of its meaning.

for instance, in Example 2, where "ridare" is the source verb and "trattenere" the target verb, the relation between the target verb and the direct object argument produces a "non sense" sentence.


Also, we think this choice helps in keeping the task more simple and intuitive for annotators than to provide a definition of the senses of the verbs.

s1 is a sentence that contains the source verb and is extracted from the annotated corpus of the T-PAs resource, while s2 is created by automatically substituting the source verb with an opposite target verb (see Table 2 for an example).


Previous work has focused on highly controlled environments, such as virtual environments as used for the GIVE-2 challenge (Gargett et al., 2010).

the image stimuli, images with tag points (coloured red/green for correct/incorrect), referring expression data and corpus annotations as described in Section 2.3. will be released as part of this submission.


In previous work (Gkatzia et al., 2015) we found that the following syntactic categories predict successful REs: NP (Noun phrases), NNP (Proper noun, singular), NN (Noun, singular or mass), JJ (Adjective) and VBN (Verb, past participle).

the corpus is annotated with the following measures of success: Reference to the type of object that is aimed to be described.


The rating of the likability was done on a seven-point Likert scale.

sVMs have been chosen as classifier since they are a well known standard method for computational paralinguistics.


The results revealed that speakers have difficulty communicating their emotions in online communication environments, regardless of the type of communication modality, and that inaccurate emotional understanding more frequently occurs in online computer-mediated communication than in face-to-face communication.

for the initial study of this project using a large-scale emotional communication corpus, the accuracy of online emotional understanding was assessed to demonstrate the emotional labels evaluated by the speakers and to summarize the speaker's answers on the questionnaire regarding the difference between the online chat and the face-to-face conversations in which they actually participated.


Then, a 2 × 3 analysis of variance (ANOVA) was performed on the factors of communication environment (Chat or FaceToFace) and chat type (VD, VD or TX) for each emotional dimension.

each speaker in the pair sat in a soundproofed room separated from the other person by soundproofed glass.


Those emotional words and emoticons can be helpful for the speaker's understanding of the partner's pleasantness.

two contradicting results were also obtained.


This difference is also observed between speakers within the same dialog, even when their speech time is relatively close: both speakers of each dialog are speaking more or less the same time but one is much more conducive to laugh than the other.

the present analysis is based on studying laughter included in Corpus of Interactional Data (Bertrand et al., 2008).


This data was stored in an AVI file and recorded also at 100 fps.

the density distributions of the mean RMS energy per affect bursts type and per arousal level are shown in Fig.


Concerning the audio, a higher arousal level was usually expressed by a higher amplitude and also by a longer duration of the sound in the "disgust" case.

this is less obvious in the case of startle and surprise.


These are due to the characteristics of acted emotions; it is stereotypical, lacking context, and limited to general and basic emotions.

arousal measures the activity of emotion; e.g.


In this paper we compare different context selection approaches to improve the creation of Emotive Vector Space Models (VSMs).

the metric used to compare the models is the F1-score at different values of K (F1@K).


Since the input data is not sentencesegmented, we use fragment pairs as unit to calculate precision and recall.

the monolingually determined sentence boundaries are not optimized for sentence alignment, because translation equivalents might cross the monolingual sentence boundaries.


We used the Cartesian product approach that adds all combinations of top words in a topic.

we now compare the baseline against the Cartesian product and sentential approaches that use multilingual topics.


Note that the sentence pairs with the low similarity scores are not necessarily the clean parallel sentences, rather not parallel sentences because they are automatically extracted from the comparable abstracts, not the direct translations of each other.

top1M only considers the top 1 million sentence pairs sorted by the similarity score, and ALL considers all the sentence pairs in the training data.


Plamada and Volk (2012) already demonstrated the difficulty in using Wikipedia categories for the extraction of domain-specific articles from Wikipedia.

given a pair of articles related by an inter-language link, the similarity between all their pairs of cross-lingual sentences are estimated using different text similarity measures.


Every pair of sentences is filled with a shade of gray proportional to the coverage measured within that particular pair of sentences; darker shades indicate greater coverage.

in our view, standalone aligners are more suited for scenarios where no parallel corpora exists for the language pair under consideration.


We are aware of more sophisticated phrase-table pruning techniques, such as the one based on statistical significance proposed by Johnson et al.

the performance of these methods degrades when the texts to be aligned are short as there is less data to support statistical inference of a bilingual lexicon.